Articles | Volume 18, issue 10
https://doi.org/10.5194/gmd-18-3065-2025
https://doi.org/10.5194/gmd-18-3065-2025
Methods for assessment of models
 | 
27 May 2025
Methods for assessment of models |  | 27 May 2025

ClimKern v1.2: a new Python package and kernel repository for calculating radiative feedbacks

Tyler P. Janoski, Ivan Mitevski, Ryan J. Kramer, Michael Previdi, and Lorenzo M. Polvani

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Cited articles

Andrews, T., Gregory, J. M., Webb, M. J., and Taylor, K. E.: Forcing, feedbacks and climate sensitivity in CMIP5 coupled atmosphere-ocean climate models, Geophys. Res. Lett., 39, https://doi.org/10.1029/2012GL051607, 2012. a
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Bonan, D. B., Kay, J. E., Feldl, N., and Zelinka, M. D.: Mid-latitude clouds contribute to Arctic amplification via interactions with other climate feedbacks, Environ. Res.-Clim., 4, 1, https://doi.org/10.1088/2752-5295/ada84b, 2025. a
Bony, S., Colman, R., Kattsov, V. M., Allan, R. P., Bretherton, C. S., Dufresne, J.-L., Hall, A., Hallegatte, S., Holland, M. M., Ingram, W., et al.: How well do we understand and evaluate climate change feedback processes?, J. Climate, 19, 3445–3482, https://doi.org/10.1175/JCLI3819.1, 2006. a
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Short summary
We developed ClimKern, a Python package and radiative kernel repository, to simplify calculating radiative feedbacks and make climate sensitivity studies more reproducible. Testing of ClimKern with sample climate model data reveals that radiative kernel choice may be more important than previously thought, especially in polar regions. Our work highlights the need for kernel sensitivity analyses to be included in future studies.
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